Preparing pseudo-pure states with controlled-transfer gates

The preparation of pseudo-pure states plays a central role in the implementation of quantum information processing in high temperature ensemble systems, such as nuclear magnetic resonance. Here we describe a simple approach based on controlled-transfer gates which permits pseudo-pure states to be prepared efficiently using spatial averaging techniques.Comment: Significantly revised and extended: now 7 pages including 3 figures; Phys. Rev. A (in press

Apparatus for ejection of an instrument cover

Apparatus for ejecting covers of instrument packages using differential pressure principl

Moral Error Theory and the Argument from Epistemic Reasons

In this paper I defend what I call the argument from epistemic reasons against the moral error theory. I argue that the moral error theory entails that there are no epistemic reasons for belief and that this is bad news for the moral error theory since, if there are no epistemic reasons for belief, no one knows anything. If no one knows anything, then no one knows that there is thought when they are thinking, and no one knows that they do not know everything. And it could not be the case that we do not know that there is thought when we believe that there is thought and that we do not know that we do not know everything. I address several objections to the claim that the moral error theory entails that there are no epistemic reasons for belief. It might seem that arguing against the error theory on the grounds that it entails that no one knows anything is just providing a Moorean argument against the moral error theory. I show that even if my argument against the error theory is indeed a Moorean one, it avoids Streumer's, McPherson's and Olson's objections to previous Moorean arguments against the error theory and is a more powerful argument against the error theory than Moore's argument against external world skepticism is against external world skepticism

Stratospheric feedback from continued increases in tropospheric methane

Tropospheric concentrations of methane have increased steadily over the past ten years at an average rate of 16.5 ppbv per year, to a value in January 1988 of 1.69 ppmv. Measurements of CH sub 4 concentrations in air bubbles trapped in ice cores have shown concentrations of about 0.7 ppmv 200 years ago, with little further change for thousands of years before that. Interpolation earlier into this century suggests a concentration of about 1.1 to 1.2 ppmv in the 1940's. The only important pathway believed to be important for transfer of air from the troposphere to the stratosphere in through the tropical tropopause which is cold enough to reduce the mixing ratio of H sub 2 O in that air to about 3 ppmv. The only other major pathway for the delivery of H to the stratosphere is through the simultaneous injection of gaseous CH sub 4 in the same rising air. The formation of clouds in the stratosphere is dependent upon very low temperatures, and generally upon the amount of water vapor available. The possibility of a positive feedback exists, especially in well-oxidized methane air, that clouds are easier to form than earlier. This could mean enhancement of PSCs in both Antarctic and Arctic locations. Additional H sub 2 O in the stratosphere can also add to some of the greenhouse calculations

Measurement of atmospheric HO by a chemical method

The parameters for a chemical technique can be outlined from the following set of desirable goals: (1) sufficient conversion of tracer species A to product B that B can be measured quantitatively in the presence of A and a great excess of air; (2) specificity of reaction such that A is converted to B only by reaction with HO; and (3) sufficient sensitivity for detection that the ambient concentration of HO is not seriously perturbed by the presence of A and B. This proposed study involves finding a chemical reaction specific enough for OH, and a measurement of the product formed. What one wants is a rate constant of about 10 to the -10th power cu cm/s, so that 0.1 percent of the OH will be converted in 100 s. Laboratory studies are needed to find a reaction which will fill this bill, yielding a product in quantity sufficient for precise measurement. This is an extremely fast constant and the search may be difficult. Again there is a question of perturbing the local environment, while still providing a sensitive measurement. Also the temperature and pressure dependence of the reaction rate is a complicated function for many of these species (that is, one must use a RRKM or Troe-based picture), and must be taken into account

On the asymptotic stability of feedback control systems containing a single time- varying element

Asymptotic stability of feedback control systems containing single time varying elemen

Integrating remote sensing datasets into ecological modelling: a Bayesian approach

Process-based models have been used to simulate 3-dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3-PG model combined with hyperspectral, LiDAR, SAR and field-based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness-of-fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field-based and remotely sensed datasets available and in estimating parameter and model output uncertainties